RECOMMENDATION ALGORITHMS FOR EDUCATIONAL PREFERENCES: A REVIEW

Educational recommender systems (ERS) are transforming the learning experience by providing personalized content and course recommendations tailored to the individual needs of students. These types of systems employ filtering approaches like collaborative and content-based, machine learning and deep learning, big data processing approaches, hybrid models to improve the educational process. The implementation of such systems persistently meets challenges like data sparsity, cold-start problems and issues related with privacy. But the recent achievements in the development of deep learning, knowledge graphs and hybrid learning give promising results of the accuracy of recommendations in e-learning. The paper studies the listed techniques above and the feasibility of engaging large datasets and explainable artificial intelligence(XAI) to boost the process of electronic education.